演習集合 的英文怎麼說

中文拼音 [yǎnxíjígě]
演習集合 英文
practice muster
  • 演 : 動詞1 （演變; 演化） develop; evolve 2 （發揮） deduce; elaborate 3 （依照程式練習或計算） drill;...
  • 集 : gatherassemblecollect
  • 合 : 合量詞（容量單位） ge, a unit of dry measure for grain （=1 decilitre）
  • 演習 : manoeuvre; exercise; drill; practice
  • 集合 : 1 （聚集） gather; assemble; muster; call together 2 [數學] [自動化] [計算機] assemblage; set; co...
  1. All crew reported to station, carried out fire drill

    航海日誌）全體船員在集合地點集合，進行消防演習。
  2. These include a four - episode tv programme on safer living, a tv programme called " meteorological series iii " jointly produced by hong kong observatory and rthk, a tropical cyclone naming contest, popular science lectures, slogan and bookmark design contests, a seminar on natural disaster reduction, a major exhibition at the hong kong science museum and rescue drill demonstrations

    除了今天的開展儀式，還有一連四集的《晴天行動》電視節目、香港天文臺及香港電臺聯合製作的電視節目《氣象萬千》第三輯、熱帶氣旋命名比賽、普及科學講座、標語創作及書簽設計比賽、減少天然災害研討會、在科學館舉行的大型展覽及拯救演習。
  3. A range of community education programmes are being organized. these include a four - episode tv programme on safer living, another tv programme " meteorological series iii " jointly produced by hong kong observatory and rthk, a tropical cyclone name nomination contest, popular science lectures, a slogan and bookmark design contest, a seminar on natural disaster reduction, a major exhibition at hong kong science museum and rescue drill demonstrations

    除了今天的開展儀式，還有一連四集的晴天行動電視節目香港天文臺及香港電臺聯合製作的電視節目氣象萬千第三輯熱帶氣旋命名比賽普及科學講座標語創作及書簽設計比賽減少天然災害研討會在科學館舉行的大型展覽及拯救演習。
  4. These include a four - episode tv programme on safer living, another tv programme " meteorological series iii " jointly produced by hong kong observatory and rthk, a tropical cyclone name nomination contest, popular science lectures, a slogan and bookmark design contest, a seminar on natural disaster reduction, a major exhibition at hong kong science museum and rescue drill demonstrations

    除了今天的開展儀式，還有一連四集的《晴天行動》電視節目、香港天文臺及香港電臺聯合製作的電視節目《氣象萬千》第三輯、熱帶氣旋命名比賽、普及科學講座、標語創作及書簽設計比賽、減少天然災害研討會、在科學館舉行的大型展覽及拯救演習。
  5. In this text, we first do some research on the genetic algorithm about clustering, discuss about the way of coding and the construction of fitness function, analyze the influence that different genetic manipulation do to the effect of cluster algorithm. then analyze and research on the way that select the initial value in the k - means algorithm, we propose a mix clustering algorithm to improve the k - means algorithm by using genetic algorithm. first we use k - learning genetic algorithm to identify the number of the clusters, then use the clustering result of the genetic clustering algorithm as the initial cluster center of k - means clustering. these two steps are finished based on small database which equably sampling from the whole database, now we have known the number of the clusters and initial cluster center, finally we use k - means algorithm to finish the clustering on the whole database. because genetic algorithm search for the best solution by simulating the process of evolution, the most distinct trait of the algorithm is connotative parallelism and the ability to take advantage of the global information, so the algorithm take on strong steadiness, avoid getting into the local

    本文首先對聚類分析的遺傳演算法進行了研究，討論了聚類問題的編碼方式和適應度函數的構造方案與計算方法，分析了不同遺傳操作對聚類演算法的性能和聚類效果的影響意義。然後對k - means演算法中初值的選取方法進行了分析和研究，提出了一種基於遺傳演算法的k - means聚類改進（混合聚類演算法） ，在基於均勻采樣的小樣本集上用k值學習遺傳演算法確定聚類數k ，用遺傳聚類演算法的聚類結果作為k - means聚類的初始聚類中心，最後在已知初始聚類數和初始聚類中心的情況下用k - means演算法對完整數據集進行聚類。由於遺傳演算法是一種通過模擬自然進化過程搜索最優解的方法，其顯著特點是隱含并行性和對全局信息的有效利用的能力，所以新的改進演算法具有較強的穩健性，可避免陷入局部最優，大大提高聚類效果。
  6. The basic thought is to divide the cities which are close to each other into a group ( physical area ) by applying sorting neural network, find out the optimal path by the improved hnn, and then calculate the local optimal path by using the same method, and finally get the whole optimal path, which are described as following : a assembly s of cities is grouped into some subsets according to their physical location and we can get, and then get the optimal, path of tsp of s = { s, i = 1, 2, n } through the given method, as well as the st

    基本思想是利用聚類神經網路先把地理位置上相互靠近的城市劃分為一個集體單位（一個物理區域） ，用改進的hopfield神經網路演算法求解各個區域間的最優（或近似最優）路徑，然後再在每一個區域內部用同樣的方法來求解其局部的最優（或近似最優）路徑，這樣可以最終得到全局的最優（或近似最優）解。描述如下：設有城市集合s ，按城市的地理位置把s劃分為若干子摘要2集，得s ijs ， ，其中廠s ； d ， i一口求得集合i叫s s ； i習， 2 ， … n的tsp最優路徑，再依次求得子集s ；內部的tsp最優路徑，即得最終優化路徑m一） s一） …一寧s ； di
  7. Based on polymerization reaction of the nylon - 6 rubberized cord fabric production of distributed control system in yangzhou organic chemical plant computer integrated manufacturing system ( yh - cims / dcs ), the multiple stepwise regression method was used to build the statistic mathematical models of the molecule weight and the monomer quantum of casting slice belt. then the optimization model of polymerization reaction was presented, which was solved by using simulation annealing algorithm to obtain the best techniques parameters. the improved hybrid genetic algorithm and back propagation algorithm are combined to train neural network, brought out the neural network prediction model of casting slice belt ' s average molecule weight to guide the technologist on - line

    提出了流程工業生產過程操作優化策略和應用實施方法，包括生產過程離線優化策略、非線性問題求解策略、在線優化模型及學習策略；結合揚州有機化工廠計算機集成製造系統集散控制系統（ yh - cims dcs ）的實施，針對錦綸? 6浸膠南京理工大學博士學位論文摘要簾于布生產中己內酚胺聚合反應過程優化控制這一工程實際問題，採用統計建模方法，建立了聚合反應過程的優化模型；為求解所得的優化模型，提出了種改進的有約束條件下的模擬退火演算法，該演算法能避免陷於局部最優解，有效地提高了所求解的全局性和可靠性：提出了基於改進的ga演算法和sp演算法相結合的混合學習演算法，建立了基於神經網路的聚合反應過程生產目標在線預測模型，該演算法和模型滿足了生產中的實時性和實用性要求。
  8. Thirdly, considering the characters of bp neural networks which is good at local minimum and bad in global optimization and the feature of ga neural networks which is bad in local minimum and good at global optimization, the paper proposes a new algorithm combined ga with bp, referred as to hybrid intelligence learning algorithm, which is applied to the problem optimizing the connection weight of the feedforward neural networks

    第三，針對bp神經網路局部搜索能力強、全局搜索能力差和基於遺傳演算法的神經網路全局搜索能力強、局部搜索能力差的特點，本文提出了一種集bp演算法和遺傳演算法優點為一體的混合智能學習法，並將其應用到優化多層前饋型神經網路連接權問題。
  9. The selected optimal individuals are clustered with rival penalized competitive learning algorithm, and for each single cluster, the factor analyzer model is used to estimate its distribution information

    首先用次勝者受罰的競爭學習演算法對選出的最優個體集合聚類，然後對每個類用因子分析模型進行分佈信息的估計。
  10. Firstly, influence factors of generalization of neural network are presented in this thesis, in order to improve neural network ’ s generalization ability and dynamic knowledge acquirement adaptive ability, a structure auto - adaptive neural network new model based on genetic algorithm is proposed to optimize structure parameter of nn including hidden layer nodes, training epochs, initial weights, and so on ; secondly, through establishing integrating neural network and introducing data fusion technique, the integrality and precision of acquired knowledge is greatly improved. then aiming at the incompleteness and uncertainty problem consisting in the process of knowledge acquirement, knowledge acquirement method based on rough sets is explored to fulfill the rule extraction for intelligent diagnosis expert system, by completing missing value data and eliminating unnecessary attributes, discretization of continuous attribute, reducing redundancy, extracting rules in this thesis. finally, rough sets theory and neural network are combined to form rnn ( rough neural network ) model for acquiring knowledge, in which rough sets theory is employed to carry out some preprocessing and neural network is acted as one role of dynamic knowledge acquirement, and rnn can improve the speed and quality of knowledge acquirement greatly

    本文首先討論了影響神經網路的泛化能力的因素，提出了一種新的結構自適應神經網路學習演算法，在新方法中，採用了遺傳演算法對神經網路的結構參數（隱層節點數、訓練精度、初始權值）進行優化，大大提高了神經網路的泛化能力和知識動態獲取自適應能力;其次，構造集成神經網路，引入數據融合演算法，實現了基於集成神經網路的融合診斷，有效地提高了知識獲取的全面性、完善性及精度;然後，針對知識獲取過程中所存在的不確定性、不完備性等問題，探討了運用粗糙集理論的知識獲取方法，通過缺損數據補齊、連續數據的離散、沖突消除、冗餘信息約簡、知識規則抽取等一系列的演算法實現了智能診斷的知識規則獲取;最後，將粗糙集理論與神經網路相結合，研究了粗糙集-神經網路的知識獲取方法。
  11. Divide the image into several regular blocks, do n ' t separate the blocks without consistent attribute and unite the blocks with the same attribute, until forming a district map. the ability that man can distinguish different goals from the complicated scenery quickly, at least partly benefit from many kinds of information in the image, such as the grey level, border, texture, etc. it illumine people to create methods on how to use mis information to segmentation. edge information is the most important high frequency information of an image

    本文以湖北省科技廳重點科技發展計劃項目資助課題? ? 「智能運輸系統的視頻信號採集及識別演算法研究」為背景，以課題中數字圖像處理部分主要環節之一? ?車牌圖像的分割問題為主要研究目標，在參閱大量文獻資料的基礎上，對數字圖像分割方法進行了學習和研究，並結合特定的數學理論，如數學形態學、小波分析等，對車牌圖像分割方法進行了著重地探討與實現。
  12. Based on the evolution of clusters, the dissertation puts forwards the cluster ' s ideal technological learning process pattern, that is : technological choosing, imitating learning, innovating learning, switchover learning ( the new technological choosing )

    最後，文章結合集群演化總結提出了產業集群技術學習的過程模式：技術選擇模仿型學習創造型學習轉換型學習（新的技術選擇） 。
  13. 2. based on the original bp network, some improvement on error back propagation arithmetic is made. the executing speed of the algorithm is increased through online adjustment of learning rate. combined with traditional pid control, this method generated two integral schemes : bp network + pid serial control and self - confirming control of parameters of pid controller based on bp network are constructed

    在原有的誤差反向傳播（ bp ）網路的基礎上，對其學習演算法進行了改進，通過在線調節學習速率，提高了演算法的實現速度，並且與傳統的比例積分微分（ pid ）控制方法進行結合，分別實現了兩種集成方法： bp網路與pid串列控制方法和基於bp網路的pid參數自整定控制方法。
  14. Based on analyzing the relationship between linear separability and a connected set in boolean space, the particular effect of a restraining neuron in extraction of rules from a bnn is discussed, and that effect is explained through a example called a mis problem in boolean space. in this paper, a pattern match learning algorithm of bnns is proposed. when a bnn has been trained by the algorithm, all the binary neurons of hidden layer belong to one or more ls series, if the logical meanings of those ls series are clear, the knowledge in the bnn can be dug out

    另一個研究成果是在分析線性可分和樣本連通性關系的基礎上，以mis問題為例，討論了抑制神經元在二進神經網路規則提取中的獨特作用，提出了二進神經網路的模式匹配學習演算法，採用這種演算法對布爾空間的樣本集合進行學習，得到的二進神經網路隱層神經元都歸屬於一類或幾類線性可分結構系，只要這幾類線性可分結構系的邏輯意義是清晰的，就可以分析整個學習結果的知識內涵。
  15. Follow along with john zukowski as he demonstrates how to iterate through the elements of a hashed collection in insertion order and how to maintain elements in access order with the new collections framework implementations in j2se, version 1. 4

    讓我們跟隨john zukowski的演示，學習如何按插入順序迭代散列集合中的各個元素，以及如何使用j2se ，版本1 . 4中的新collections framework按存取順序維護元素。
  16. The important research is about the theory and methods of the cluster analysis in view of statistical theory, the theory and methods of fuzzy cluster analysis, the fkn " s structure and the fkn ' s study algorithm ( fkn, fuzzy kohonen network ) - the organic fusion of the fuzzy c - means algorithm and self - organized feature map neural network. the paper proposes the ifkn ( improved fkn ) on the basis of the hard classification idea and the soft classification idea, then carries on the cluster analysis of the artificial synthetic control chart time series through matlab program and tt ? cluster result matches the cluster result of the famous dataengine " s software of the intellectual data analysis and data mining from german mit company. finally, the paper discusses the applying of the cluster analysis to the control process, which can be widely applied to the pattern recognition of the parameter " s changing trend during the control process and the image partition processing, and utilizes the ifkn to recognize the thermotechnical parameter " s changing trend based on the engineering of clinker sintering rotary kiln automatic control system of guizhou " s aluminium factory, through which good effect is obtained

    數據挖掘技術在商業領域中已廣泛使用，然而在工業過程式控制制中的應用卻極少，本文正是在這種背景下，對數據挖掘中的聚類分析方法及其在工業過程式控制制中的應用研究作了償試，重點研究了基於統計理論的聚類分析理論和方法，模糊聚類分析理論和方法及模糊kohonen網路（ fkn ）的結構與學習演算法，即模糊c ? ?均值演算法與自組織特徵映射神經網路（ kohonen網路）的有機融合，並根據硬分類思想及軟分類思想提出了改進的模糊kohonen網路（ ifkn ） ，通過matlab編程對人工合成控制時序圖數據集進行聚類分析，其聚類效果與當今廣泛使用的數掘挖掘軟體平臺，德國mit公司著名的dataengine智能數據分析和數掘挖掘軟體的聚類效果相當，最後，論述了聚類分析在控制中的應用，它可以用於過程式控制制中的參數變化趨勢的模式識別及圖象分割處理等具體應用中，並以貴州鋁廠熟料燒結回轉窯自動控制系統為工程背景，利用ifkn識別其熱工參量變化趨勢，取得了較理想的效果。
  17. Thus a collection of reciprocally typified actions will emerge, habitualized for each in roles, some of which will be performed separately and some in common

    因此交互類型行動的集合得以出現、成了每個角色的習慣，有些行動是個別地表演，有些一起表演。
  18. So, this dissertation focuses on the evolution of technological capability of these clusters and starts out to discuss the issue of technological learning of them. the total objective of our discussion is to conclude a reasonable and effective learning mechanism for these clusters that can integrate clustering members to coordinated technological learning activities so as to push the capability development of their cluster

    鑒於此，本文著眼于集群技術能力的演進，探討了集群技術學習的問題，目的在於總結一種能有效整合集群內部各學習主體的學習機制，以推動集群整體的技術學習和能力發展。
  19. This paper studies a design method of decentralized signal detection system which consists of adaptive fuzzied local - detectors and a data fusion rule of on - line self - learning weights. the local - detectors for inaccurate signal parameters are modeled by means of fuzzy sets which can be adapted to change of the inaccurate signal parameteres. the data fusion center where the optimal declsion rules are used as objective function can learn the local decision weights on - line. the robustness of the fuzzied local - detectors and the adaptability of the self - learned fusion rule make it true that the detection performance of the decentralized detection system is improved under uncertainty and this system can also process the decentralized signal detection with a unknown parameter of unknown distribution or non - random unknown parameter

    本文研究了一種由局部自適應模糊檢測器和在線自學習融合演算法所構成的分散式信號檢測系統的設計方法.由模糊集對不精確信號參數的局部檢測器進行建模，該模糊模型可自適應不精確信號參數的變化.融合中心以最佳融合規則作為目標函數在線自學習局部判決的權重.局部模糊檢測器的魯棒性和自學習融合演算法的自適應性使該分散式檢測系統在不確定環境下的檢測性能得到提高.也使該系統能夠處理未知分佈的未知參數以及非隨機未知參數的分散式信號檢測
  20. In this paper, we introduce a new architecture, which stands for fuzzy neural network on the base of the standard additive model, and investigate some learning and adaptation strategies associated with the fuzzy sets

    在本文中我們提出一種以標準可加性模型為基礎，把模糊系統和神經網路相結合的新結構，並給出模糊集合學習和調整的新學習演算法。
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